An Application of Generalized Linear Models to Fine Needle Aspiration in Breast Cancer

Abstract

Breast cancer is currently the most dangerous cancer for women worldwide. Doctors routinely employ biopsies, diagnostic mammograms, and other techniques to detect and diagnose breast cancer. The Fine Needle Aspiration, also referred to as fine needle biopsy, is a technique for assessing tumors that involves inserting a needle into a mass to retrieve alive cells. However, the current breast biopsy test is time-consuming and unable to detect early breast cancer. Applying the statistical tools to fine-needle aspiration is helpful in developing its feasibility and reducing test time, thereby reducing the cost of service as well as waiting time. In this study, the diagnostic model was fitted with a generalized linear model as the framework and Least Absolute Shrinkage and Selection Operator regression as the essential methods. Amongst cellular level features, which are variables in the model, some features were identified that play an essential role in the models, including texture, smoothness, concave points, and fractal dimension. The high accuracy (>0.9) obtained from the model in data testing supported that Generalized-Linear-Models-based machine prediction can effectively assist physicians in their clinical diagnosis. In addition, essential features in the model could be considered to have some association with the hidden lesion of breast cancer.